Thinking about platforming with more traditional mediatization: Lessons from audiovisual analysis
Bibliographic record
Abstract
Since the mid-1990s, film and television industries are more and more confronted with the appearance of new intermediation services which have created platforms. In a project funded by the Social Sciences and Humanities Research Council of Canada (SSHRC), we try to analyze the place and role that these new services are taking in the audiovisual sector. Our corpus is composed of the platforms of four companies that have developed activities on a vast international scale, Netflix (with its service of the same name), Amazon (Prime Video service), Disney (Disney+) and Apple (Apple TV+). Based on our corpus, it seems to us that some changes have been the result of firms’ activities, but that it is not as linear as it may appear at first sight. Transformations are at work but there is also some “Old Media Persistence.” Thus, we find a certain “contamination” of old practices originating from the organization of industrial channels and forms in the mutations currently presented by these new intermediation services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.014 | 0.033 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".